Automated Vehicle Digital Map Change Prediction
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Solution Overview
Problem
Current methods for predicting changes in a static environment for automated vehicles using digital maps are inadequate, leading to increased failure rates and safety concerns due to outdated map data.
Innovation Solution
A method and system that identify deviations in the environment using sensor data and databases, quantify these deviations with change indicators, and predict future changes in the digital map, thereby reducing the failure rate and improving safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If digital map data is used for automated vehicle guidance, then navigation accuracy is improved, but map data becomes outdated over time due to infrastructure changes, increasing the failure rate
Solution Approach 1:
The system performs preliminary actions by detecting and quantifying infrastructure changes before they completely invalidate the digital map. Change indicators are calculated in advance based on sensor data deviations, allowing the system to predict future map failures and trigger updates proactively rather than reactively, maintaining both accuracy and reliability
Solution Approach 2:
The system establishes a continuous feedback loop where sensor data from automated vehicles is constantly compared against digital map data, deviations are quantified into change indicators, and this feedback triggers map updates when thresholds are exceeded. This closed-loop system ensures the digital map remains accurate while minimizing failure rates through timely updates
2Loss of information
If frequent map updates are performed to maintain accuracy, then map currentness is improved, but system complexity and update costs increase
Solution Approach 1:
The system changes the parameter of map update frequency from fixed/scheduled to dynamic/adaptive based on calculated change indicators. Instead of updating maps at regular intervals or only upon detected changes, the system continuously monitors deviation parameters and triggers updates only when change indicators exceed predetermined thresholds, optimizing the balance between map currentness and system complexity
Solution Approach 2:
The system enables self-service by allowing automated vehicles to contribute sensor data that automatically feeds into change detection algorithms. The vehicles themselves participate in map maintenance by providing real-world observation data, reducing the need for centralized survey operations and lowering overall system complexity while improving map currentness
3Measurement precision
If change detection thresholds are set low to improve sensitivity, then detection accuracy is improved, but false positives increase leading to unnecessary map updates
Solution Approach 1:
The system applies multi-functionality by using change indicators serving multiple purposes: they detect infrastructure changes, quantify deviation severity, predict future map failures, and trigger appropriate responses (alerts, updates, or continued monitoring). This universal metric reduces false positives by providing a comprehensive assessment that distinguishes significant changes from minor variations, improving detection accuracy while reducing unnecessary updates
Data Source
AI summary
A method for predicting changes in a static environment of an automated vehicle with respect to a digital map. The digital map includes at least information about a road layout and static objects in the environment of the automated vehicle, comprises the following method steps. Deviations in the environment of the automated vehicle with respect to the digital map are identified on the basis of sensor data from at least one sensor and/or at least one database, and the identified deviations are quantified by ascertaining at least one change indicator. At least one probability of future changes in the environment of the automated vehicle with respect to the digital map is ascertained on the basis of the identified and quantified deviations.